A10 vs T4
A10 has 1.5× the memory; A10 has 1.9× the bandwidth; T4 rents for 7.5× less.
The NVIDIA A10 (Ampere, 2021) and the NVIDIA T4 (Turing, 2018) are 3 years and a silicon generation apart, which makes the price gap the heart of the story. On capacity, the A10 carries 1.5x the memory (24 vs 16 GB), which sets what each can hold at all. On memory bandwidth, the number that governs LLM serving speed, the A10 leads at 0.6 vs 0.32 TB/s. Raw compute favors the A10 by 1.9x, which matters for training, prefill-heavy traffic, and diffusion work more than for chat-style decoding.
Price is where it settles: the T4 rents from $0.10/hr against $0.75/hr for the A10, a 7.5x gap that the performance numbers above only partly close. Per gigabyte of VRAM, the T4 is the cheaper rental, worth knowing if your model is capacity-bound rather than speed-bound. For how these numbers translate into tokens per dollar, the inference cost estimator runs both cards against any model. Everything below is the underlying data.
NVIDIA · Ampere · 2021
NVIDIA A10
The quiet default for 7B-class production inference on AWS and Oracle.
From $0.75/hr at Lambda
Head to head
■ A10 ■ T4, bars share one scale across the whole catalog.
| A10 | T4 | |
|---|---|---|
| Memory | 24 GB GDDR6 | 16 GB GDDR6 |
| Bandwidth | 0.6 TB/s | 0.32 TB/s |
| FP16 dense | 125 TF | 65 TF |
| FP8 dense | - | - |
| TDP | 150 W | 70 W |
| Interconnect | PCIe Gen4 | PCIe Gen3 |
| Cheapest rental | $0.75/hr | $0.10/hr |
| $/hr per GB VRAM | $3.1¢ | $0.6¢ |
What one can run that the other can't
Only on A10
- Gemma 2 27B (4-bit)
- gpt-oss-20b (4-bit)
Only on T4
Nothing, A10 runs everything T4 does.
Rule of thumb: for LLM serving, prefer the GPU with more memory bandwidth per dollar; for training and prefill-heavy work, prefer FLOPS per dollar; and if the model doesn't fit in VRAM, none of the other numbers matter. Sanity-check with the VRAM calculator.